CN113222271A - Medium and small airport site selection layout method under comprehensive transportation system - Google Patents
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Abstract
The invention provides a medium and small airport site selection layout method under a comprehensive transportation system, which comprises the following steps: establishing a traffic association degree model after defining the regional boundaries of the multi-airport system, and dividing the traffic areas of the multi-airport system; establishing a passenger flow index system, reducing the passenger flow index system, then establishing a neural network model, and predicting the total outward travel amount of each traffic area; a prospect theory is introduced, and a Bayesian network is used for correcting a traditional discrete selection model to obtain the civil aviation trip sharing rate; establishing a node importance index system for nodes of a transportation network based on an airport of a multi-airport system area; and after fuzzy mathematics and cluster analysis are introduced, an alternative scheme set of the airport site is obtained through screening, and an airport layout mathematical model is established based on the alternative scheme set and the civil aviation travel passenger flow to obtain the optimal airport site. The method is based on competition and coordination among airports in a multi-airport system and competition and coordination among civil aviation and other traffic modes, and site selection and layout of medium and small airports are achieved.
Description
Technical Field
The invention belongs to the technical field of crossing of transportation and airport site selection, and particularly relates to a site selection layout method for small and medium airports under a comprehensive transportation system.
Background
The airport site selection and layout are used as the early-stage work of airport construction, are important links of airport planning and construction, relate to various factors, and are a multi-objective optimization decision problem with strong comprehensiveness. With the increase of the number of airports in the multi-airport system area, the multiple airports, the highway and the high-speed railway share the common transportation of the area to the outside, and the competition among the multiple airports in the area and between civil aviation and other transportation modes is more and more intense.
At present, the research on airport site selection problems in China can be roughly divided into four categories, namely, the concept of researching the airport site selection; starting from a specific new airport, carrying out empirical study; establishing an airport site selection model; the information research is carried out by depending on a geographic information system and a computer technology. The method has the characteristics that the method is concentrated on single airport site selection, the layout among a plurality of airports in the same area is less considered, the competition and coordination among the airports are neglected, a systematic airport site selection and layout theoretical system is not formed, the site selection problem of the airports is considered from the viewpoint of the airports, and the influence of the mutual competition between civil aviation and other traffic modes on the airport site selection and layout is less considered. Meanwhile, airport site selection relates to complex factors in various aspects such as geographic positions, landforms and the like, a multi-attribute decision method is mostly adopted for evaluating a site selection scheme of a new airport in decision, the method is mainly qualitative and quantitative, and the decision result is not objective. The passenger demand attention degree is not enough, which results in the insufficient passenger flow after the airport is built.
The research on airport site selection and layout in foreign countries is mainly reflected in the problem of how to select passengers in the traveling process when a plurality of airports exist in an area, and the research focuses on the selection behavior of the departure airport and has less research on the layout of the airports in a multi-airport system because most of the airports are in large quantity and are in long-distance traveling.
In summary, the traditional airport location selection starts from a single newly-built airport, other airports and other traffic modes in the whole multi-airport system area are not considered sufficiently, the attention degree of the travel demand of passengers is not enough, and competition and coordination among the airports in the multi-airport area and between civil aviation and other traffic modes are less considered, so that the layout of the airports is unreasonable, the transportation capacity of the airports is not enough, and the operation is difficult.
Disclosure of Invention
One of the purposes of the invention is to provide a site selection and layout method for medium and small airports under a comprehensive transportation system, which can be used for site selection and layout planning of the medium and small airports.
In order to achieve the purpose, the technical scheme of the invention is as follows: a method for site selection and layout of medium and small airports under a comprehensive transportation system comprises the following steps: on the basis of defining the boundary of the multi-airport system area, a traffic association degree model is established, and traffic areas of the multi-airport system area are divided based on spatial statistical analysis;
establishing a passenger flow index system influencing passenger flow, reducing the index system by combining a rough set and a principal component analysis method, then constructing a neural network model, and predicting the total outward travel amount of each traffic area;
a prospect theory is introduced, and a Bayesian network is used for correcting a traditional discrete selection model to obtain the civil aviation trip sharing rate;
constructing a node importance index system based on the airport of the multi-airport system area as a node of a transportation network;
fuzzy mathematics and cluster analysis are introduced to determine an initial selection scheme set of the airport sites.
Further, the passenger flow volume index system comprises a regional population index, a social and economic development index and a comprehensive traffic development index of the multi-airport system.
Further, the step of establishing a passenger flow volume index system influencing passenger flow volume, combining a rough set and a principal component analysis passenger flow volume reduction index system, then constructing a neural network model, and predicting the total amount of outgoing calls of each traffic area specifically comprises the following steps:
establishing a passenger flow index system, and reducing the passenger flow index system by combining a rough set and a principal component analysis method;
constructing a neural network model, and introducing a random forest algorithm to train the neural network model;
and (4) introducing interval number and grey prediction models to respectively predict the passenger flow index system, inputting the predicted values as trained neural network models, and outputting the input values as the total amount of outgoing.
Further, the step of introducing the foreground theory to modify the traditional discrete selection model by means of the bayesian network to obtain the civil aviation trip sharing rate specifically includes:
constructing a traditional discrete selection model, and taking the output of the traditional discrete selection model as conditional distribution;
introducing a foreground theoretical model, and taking output as prior distribution;
correcting the output of the traditional discrete selection model by using the output of the foreground theoretical model through the Bayesian network;
and obtaining the sharing rate of civil aviation trips.
Further, the node importance index system includes: geographical location index, terrain index, landform index, weather index, ground traffic index, power supply index, gas supply index, communication index, road index, and oil supply index.
Further, the step of determining the initial selection scheme set of the airport site by introducing fuzzy mathematics and cluster analysis specifically comprises the following steps:
normalizing the indexes in the node importance index system, performing dimensionality reduction on the node importance index system by using a rough set method, and then acquiring the weight of each index through information entropy;
fuzzy mathematics and cluster analysis are introduced to determine an initial selection scheme set of the airport sites.
Further, the method also comprises the following steps:
after the primary selection scheme set is obtained, screening the primary selection scheme set by using multi-objective optimization and discrete mathematics to obtain an alternative scheme set;
establishing an airport layout mathematical model taking the lowest comprehensive travel cost of passengers as a target function based on the alternative scheme set and the total outward travel amount of civil aviation;
and solving the airport layout mathematical model to obtain an optimal scheme.
Further, the airport layout mathematical model is a nonlinear integer stochastic programming model.
Further, a three-stage algorithm is adopted to solve the airport layout mathematical model.
Further, the airport layout mathematical model is solved by:
converting the nonlinear programming problem into a plurality of linear integer random programming sub-problems by adopting an enumeration method;
converting the random programming subproblem into an integer programming problem by adopting a Monte Carlo method;
solving an integer programming problem based on a genetic algorithm;
and obtaining a solution set after sampling for many times, and finally determining the optimal solution by adopting a statistical analysis and random sampling method.
Compared with the prior art, the invention has the following advantages:
under a comprehensive transportation system, the invention takes the coordinated development of various traffic modes as the premise, fully considers the competition among airports in a multi-airport system and the competition between civil aviation and other traffic modes, and takes the trip demand of passengers as the main body after analyzing the influence factors of airport site selection and layout, thereby solving the site selection and layout problems of medium and small airports; meanwhile, by adopting the theory and method of system analysis and introducing the travel behavior theory of passengers, a complete set of airport site selection and layout planning theoretical system is established to make up for the defects of the existing airport site selection and layout theory. The invention can provide theoretical support and technical means for planning, site selection and layout of airport construction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive exercise.
FIG. 1 is a block diagram illustrating a traffic zone division process for multi-airport system areas according to the present invention;
FIG. 2 is a block diagram of a process of obtaining the outbound total amount according to the present invention;
FIG. 3 is a block diagram of a process of obtaining a distribution rate of civil aviation trips in the present invention;
FIG. 4 is a block diagram of an alternate set of flows for acquiring an airport site of the present invention;
fig. 5 is a block diagram of a scheme for selecting an optimal airport site according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The examples are given for the purpose of better illustration of the invention, but the invention is not limited to the examples. Therefore, those skilled in the art should make insubstantial modifications and adaptations to the embodiments of the present invention in light of the above teachings and remain within the scope of the invention.
The embodiment provides a medium and small airport site selection layout method under a comprehensive transportation system, which comprises the following steps:
s1: on the basis of defining the boundary of the multi-airport system area, a traffic association degree model is established, and traffic areas of the multi-airport system area are divided based on spatial statistical analysis;
in the step, the range of the multi-airport system area is determined, the boundary range of the multi-airport system is defined, a method for converting the time of passengers into value is determined, the problem that the time and the ticket price dimension are different and the modeling cannot be carried out is solved, the traffic areas in the same area are subdivided according to the characteristics of traffic transportation, and a foundation is laid for analyzing the travel requirements of the passengers;
referring to fig. 1, a specific process flow diagram of the multi-airport system can be referred to, specifically, the theory and method of system engineering are adopted to comprehensively analyze the constituent elements, the relation among the elements and the hierarchical structure among the elements of the multi-airport system, and define the boundary range of the multi-airport system; starting from the travel behavior of the passenger, analyzing the time value among different transportation modes when the passenger has multiple transportation modes in the travel process; the concept of traffic association degree is put forward on the basis of spatial statistical analysis, and parameters such as a spatial association matrix, the traffic association degree and the like are modeled by adopting a spatial clustering analysis model to establish a traffic region division method.
S2: establishing a passenger flow index system influencing passenger flow, reducing the passenger flow index system by combining a rough set and a principal component analysis method, then constructing a neural network model, and predicting the total outward travel amount of each traffic area;
in the step, an index system influencing passenger flow is established, the index system is reduced by combining a rough set and a principal component analysis method, then a neural network model is established, and after the predicted values of all indexes are determined by introducing an interval number and a gray prediction model, the interval number and the gray prediction model are substituted into the neural network model to predict the total outward travel amount of all traffic areas; specifically, the method comprises the following steps:
the total outgoing amount refers to the passenger demand of one of the departure place or the destination outside the multi-airport system, and the outgoing amount needs to borrow outgoing traffic modes which are long-distance automobile transportation, railways and civil aviation; on the basis of carrying out traffic area division on a multi-airport system area, the total outward travel amount of each traffic area needs to be predicted, the amount controls the travel amount shared by civil aviation, and the situation that decision-making errors are caused by large prediction amount caused by the travel amount prediction from an airport is avoided;
in this embodiment, the process of calculating the total amount of outgoing calls can refer to fig. 2, and the social economic development prediction and the population development prediction are combined on the basis statistical data such as the population economy; based on the prediction of the average number of people going out, the prediction of the average number of people going out and the prediction of the distance of people going out, the total amount of the outgoing lines in the range of the multi-airport system is determined by combining the factors such as regional development planning, and the like, the specific process is as follows: firstly, establishing a passenger flow volume index system influencing passenger flow volume, wherein the passenger flow volume index system comprises a multi-airport system area population index, a social and economic development index, a comprehensive traffic development index and other influencing factors; then reducing a passenger flow index system by combining a rough set and a principal component analysis method, constructing a neural network model on the basis, and introducing a random forest algorithm to train the network so as to improve the model precision; then, respectively predicting passenger flow size influence factors, wherein the prediction process can take the planned year selected by an airport as a unit, and certainly can also be other factors, the predicted value is input as a trained neural network, the output is the total amount of regional outward travel, and if the passenger flow size influence factors of the planned year are respectively predicted, the total amount of regional outward travel of the planned year is output;
preferably, because the predicted value of the influencing factor has a certain uncertainty, an uncertainty theory is introduced to study the predicted value, so that the reliability of prediction is improved;
s3: modifying the traditional discrete selection model by means of a Bayesian network based on a foreground theory to obtain the civil aviation trip sharing rate;
the transportation modes adopted by the regional outward trip of the multi-airport system are various, such as railways, highways, air transports and the like, the market share of the civil aviation can be obtained in the outward trip of the whole region, the competitive advantage of the civil aviation in the transportation mode of the whole outward trip is determined, and the advantage is larger and the sharing rate is higher. Therefore, the determination of the trip sharing rate of the civil aviation is the basis for determining the total outward trip amount of the civil aviation; in the step, modeling analysis is performed on the allocation rate, a specific flow chart can refer to fig. 3, on the basis of analyzing the travel mode selection attribute, the travel decision rule, the available information of a decision maker and the like related to the individual travel behavior by adopting a consumer behavior theory, a traditional discrete selection model is firstly constructed, then a foreground theory is introduced to modify the traditional discrete selection model by means of a bayesian network so as to improve the model accuracy, and finally the allocation rate of civil aviation is obtained.
S4: constructing a node importance index system and determining a quantization method of the index system;
in the step, the airport of the multi-airport system area is considered as a node of the transportation network, the airport site selection and layout problem is a complex problem with multiple elements, and the influence factor of deeply excavating the airport site selection and layout is a prerequisite condition of the airport site selection and layout planning. Therefore, a node importance index system is constructed in the step. Specifically, in the step, starting from aspects such as geographic position, terrain, landform, engineering geology, hydrogeological conditions, clearance conditions, meteorological conditions, ground traffic conditions, various original facility conditions on the ground and underground, public facility conditions such as power supply, water supply, gas supply, communication, roads, drainage and the like, oil supply conditions and the like, influence factors of airport layout are analyzed, and a corresponding index system is established; the method comprises the steps of introducing a complex network theory and a traffic region theory, determining the importance of air transportation in transportation network nodes (near each city), constructing an air transportation importance index system in the network nodes, and determining a quantization method of the index system.
S5: fuzzy mathematics and cluster analysis are introduced to determine a primary selection scheme set of the airport site, and an alternative scheme set is formed after screening;
the method comprises the specific steps of determining a primary selection scheme of the airport site selection and layout by combining the influence factors of the airport, referring to fig. 4, specifically, after normalizing each index of the index system in the step S4, introducing a rough set method to reduce the dimension of the index system, determining the weight of each index by using the information entropy, and then introducing a fuzzy mathematical theory and a cluster analysis theory to determine a primary selection scheme set of the airport site from the angle of combining the subjective and objective analysis of decision theory and multivariate statistical analysis.
Further, after the primary selection scheme set is obtained, the schemes can be screened, some obvious unreasonable schemes are eliminated, and an alternative scheme set is formed.
S6: and selecting an optimal scheme.
In this step, based on the alternative solution set obtained in step S5 and the total amount of outgoing lines of civil aviation in S2 and S3, a mathematical model for airport layout optimization is established in combination with the characteristics of outgoing behavior of passengers, and an optimal solution is selected, that is, where an airport is reasonably established, and how many airports are most suitable for establishing in a multi-airport system area, and the process can refer to fig. 5. Specifically, on the basis of obtaining alternative schemes and the total outward travel amount of each traffic area in the area, an airport layout mathematical model which takes the lowest comprehensive travel cost of passengers as an objective function is established, and the airport layout mathematical model is a nonlinear integer stochastic programming model due to certain uncertainty of civil aviation travel amount of each traffic area; then designing a three-stage algorithm to solve the model: converting a nonlinear programming problem into a plurality of linear integer random programming subproblems by adopting an enumeration method, converting the random programming subproblems into integer programming problems by adopting a Monte Carlo method, and solving an integer programming model by adopting a genetic algorithm; and obtaining a solution set after multiple sampling, and finally determining the optimal solution by adopting a statistical analysis and random sampling method.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for site selection and layout of medium and small airports under a comprehensive transportation system is characterized by comprising the following steps:
on the basis of defining the boundary of the multi-airport system area, a traffic association degree model is established, and traffic areas of the multi-airport system area are divided based on spatial statistical analysis;
establishing a passenger flow index system influencing passenger flow, reducing the index system by combining a rough set and a principal component analysis method, then constructing a neural network model, and predicting the total outward travel amount of each traffic area;
a prospect theory is introduced, and a Bayesian network is used for correcting a traditional discrete selection model to obtain the civil aviation trip sharing rate;
constructing a node importance index system based on the airport of the multi-airport system area as a node of a transportation network; fuzzy mathematics and cluster analysis are introduced to determine an initial selection scheme set of the airport sites.
2. The method of claim 1, wherein the passenger flow volume indicator system comprises a multiple airport system area population indicator, a socioeconomic development indicator, and a composite traffic development indicator.
3. The method according to claim 1, wherein the step of establishing a passenger flow volume index system influencing passenger flow volume, reducing the passenger flow volume index system by combining a rough set and a principal component analysis method, then constructing a neural network model, and predicting the total amount of outgoing calls of each traffic area specifically comprises the steps of:
establishing a passenger flow index system, and reducing the passenger flow index system by combining a rough set and a principal component analysis method;
constructing a neural network model, and introducing a random forest algorithm to train the neural network model;
and (4) introducing interval number and grey prediction models to respectively predict the passenger flow index system, inputting the predicted values as trained neural network models, and outputting the input values as the total amount of outgoing.
4. The method according to claim 1, wherein the step of obtaining the allocation rate of civil aviation trips by modifying the traditional discrete selection model by introducing the prospect theory and borrowing the bayesian network specifically comprises:
constructing a traditional discrete selection model, and taking the output of the traditional discrete selection model as conditional distribution;
introducing a foreground theoretical model, and taking output as prior distribution;
correcting the output of the traditional discrete selection model by using the output of the foreground theoretical model through the Bayesian network;
and obtaining the sharing rate of civil aviation trips.
5. The method of claim 1, wherein the node importance index system comprises: geographical location index, terrain index, landform index, weather index, ground traffic index, power supply index, gas supply index, communication index, road index, and oil supply index.
6. The method of claim 1, wherein the step of introducing fuzzy mathematics and cluster analysis to determine a set of preliminary solutions for airport sites specifically comprises:
normalizing the indexes in the node importance index system, performing dimensionality reduction on the node importance index system by using a rough set method, and then acquiring the weight of each index through information entropy;
fuzzy mathematics and cluster analysis are introduced to determine an initial selection scheme set of the airport sites.
7. The method according to any one of claims 1-6, further comprising the step of:
after the primary selection scheme set is obtained, screening the primary selection scheme set by using multi-objective optimization and discrete mathematics to obtain an alternative scheme set;
establishing an airport layout mathematical model taking the lowest comprehensive travel cost of passengers as a target function based on the alternative scheme set and the total outward travel amount of civil aviation;
and solving the airport layout mathematical model to obtain an optimal scheme.
8. The method of claim 7, wherein the airport layout mathematical model is a non-linear integer stochastic programming model.
9. The method of claim 7, wherein the airport layout mathematical model is solved using a three-stage algorithm.
10. The method of claim 8, wherein the airport layout mathematical model is solved by:
converting the nonlinear programming problem into a plurality of linear integer random programming sub-problems by adopting an enumeration method;
converting the random programming subproblem into an integer programming problem by adopting a Monte Carlo method;
solving an integer programming problem based on a genetic algorithm;
and obtaining a solution set after sampling for many times, and finally determining the optimal solution by adopting a statistical analysis and random sampling method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114724414A (en) * | 2022-03-14 | 2022-07-08 | 中国科学院地理科学与资源研究所 | Method, device, electronic equipment and medium for determining urban air traffic sharing rate |
| CN114880819A (en) * | 2022-03-26 | 2022-08-09 | 云南省设计院集团有限公司 | Space optimization method based on town node importance degree and traffic area bit line research |
| CN115860486A (en) * | 2023-02-22 | 2023-03-28 | 中国民用航空总局第二研究所 | Method and device for determining airport operation importance degree, electronic equipment and medium |
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